ROUGE 2.0 is an easy to use evaluation toolkit for Automatic Summarization tasks. It uses the ROUGE system of metrics which works by comparing an automatically produced summary or translation against a set of reference summaries (typically human-produced). ROUGE is one of the standard ways to compute effectiveness of auto generated summaries. To understand how ROUGE works …
ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It is essentially of a set of metrics for evaluating automatic summarization of texts as well as machine translation. It works by comparing an automatically produced summary or translation against a set of reference summaries (typically human-produced). This article provides an intuitive explanation of how ROUGE works.
While much work has been done in the area of extractive summarization, there has been limited study in abstractive summarization as this is much harder to achieve (going by the definition of true abstraction). This page contains a very small collection of summarization methods that are non-extractive…
This article talks about how to work with ROUGE for evaluation of summarization tasks. The original ROUGE toolkit uses a perl implementation which is really hard to understand, so I decided to piece together some information that may be helpful to others…